Visual analytics of 3D LiDAR point clouds in robotics operating systems

Alia Mohd Azri, Shuzlina Abdul-Rahman, Raseeda Hamzah, Zalilah Abd Aziz, Nordin Abu Bakar


This paper presents visual analytics of 3D LiDAR point clouds in robotics operating system. In this study, experiment on simultaneous localization and mapping (SLAM) using point cloud data derived from the light detection and ranging (LiDAR) technology is conducted. We argue that one of the weaknesses of the SLAM algorithm is in the localization process of the landmarks. Existing algorithms such as grid mapping and monte carlo have limitations in dealing with 3D environment data that have led to less accurate estimation. Therefore, this research proposes the SLAM algorithm based on real-time appearance-based (RTAB) and makes use of the red green blue (RGB) camera for visualisation. The algorithm was tested by using the map data that was collected and simulated on the robot operating system (ROS) in Linux environment. We present the results and demonstrates that the map produced by RTAB is better compared to its counterparts. In addition, the probability for the estimated location is improved which allows for better vehicle maneuverability.


Autonomous vehicle; LiDAR; Localization; Mobile robot; RTAB-map; Simulation

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